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Type of Document Dissertation
Author Swangmuang, Nattapong
Author's Email Address nswang@mail.sis.pitt.edu
URN etd-10132008-122243
Title A Location Fingerprint Framework Towards Efficient Wireless Indoor Positioning Systems
Degree Doctor of Philosophy
Program Information Science
School School of Information Sciences
Advisory Committee
Advisor Name Title
Dr. Prashant Krishnamurthy Committee Chair
Dr. Ching-Chung Li Committee Member
Dr. Hassan Karimi Committee Member
Dr. Martin Weiss Committee Member
Dr. Richard Thompson Committee Member
Keywords
  • performance model
  • efficient radio map
  • location fingerprint
  • indoor position location system
Date of Defense 2008-09-11
Availability restricted
Abstract
Location of mobile computers, potentially indoors, is essential information to enable locationaware

applications in wireless pervasive computing. The popularity of wireless local area networks (WLANs) inside and around buildings makes positioning systems based on readily available received signal strength (RSS) from access points (APs) desirable. The fingerprinting technique associates location-dependent characteristics such as RSS values from multiple APs to a location (namely location fingerprint) and uses these characteristics to infer the location. The collection of RSS fingerprints from different locations are stored in a database called radio map, which is later used to compare to an observed RSS sample vector for estimating the MS’s location.

An important challenge for the location fingerprinting is how to efficiently collect fingerprints

and construct an effective radio map for different indoor environments. In addition, analytical models to evaluate and predict “precision” performance of indoor positioning systems based on location fingerprinting are lacking. In this dissertation, we provide a location fingerprint framework that will enable a construction of efficient wireless indoor systems. We develop a new analytical model that employs a proximity graph for predicting performance of indoor positioning systems based on location fingerprinting. The model approximates

probability distribution of error distance given a RSS location fingerprint database and its associated statistics. This model also allows a system designer to perform analysis of the internal structure of location fingerprints. The analytical model is employed to identify and eliminate unnecessary location fingerprints stored in the radio map, thereby saving on computation while performing location estimation. Using the location fingerprint properties such as clustering is also shown to help reduce computational effort and create a more scalable model. Finally, by study actual measurement with the analytical results, a useful guideline for collecting fingerprints is given.
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